Nitrogen removal from activated sludge wastewater treatment systems is an energy-intensive process due to the large aeration requirement for nitrification. This energy footprint could be minimized with engineering control strategies that wash out nitrite-oxidizing bacteria (NOB) to limit oxygen demands.

Three laboratory-scale moving bed biofilm reactors (MBBR) with different carrier filling ratios ranging from 40% to 60% were used to study the effects of carrier-attached biofilm on oxygen transfer efficiency. In this study, we evaluated the performance of three MBBRs in degrading chemical oxygen demand and ammonia. The three reactors removed more than 95% of NH þ 4 -N at an air flow-rate of 60 L$h -1 . The standard oxygen transfer efficiency (αSOTE) of the three reactors was also investigated at air flow-rates ranging from 60 to 100 L$h -1 . These results were compared to αSOTE of wastewater with a clean carrier (no biofilm attached). Results showed that under these process conditions, αSOTE decreased by approximately 70% as compared to αSOTE of wastewater at a different carrier-filling ratio. This indicated that the biofilm attached to the carrier had a negative effect on αSOTE. Mechanism analysis showed that the main inhibiting effects were related to biofilm flocculants and soluble microbial product (SMP). Biofilm flocs could decrease αSOTE by about 20%, and SMP could decrease αSOTE by 30%-50%.

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting Nitrospira population genomes belonging to two sublineages, which exhibited distinct growth strategies and underwent a compositional shift following the selective pressure. A trait-based process model calibrated at the NOB genus level better predicted nitrite accumulation than a conventional process model calibrated at the NOB guild level. This work demonstrates that trait-based modeling can be leveraged to improve our prediction, control, and design of functionally diverse microbiomes driving key environmental biotechnologies.

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